Efficient network training for DOA estimation

Kar Ann Toh, Chong Yee Lee

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


In this paper, we treat estimation of DOA in mobile communications as a mapping problem. A multilayer Feedforward Neural Network (FNN) is proposed to establish such a map. Main advantage for a trained FNN is that DOA estimation becomes simple and cost-effective for real-time applications. Since training of FNN by the popular backpropagation algorithm usually requires a large number of training iterations to attain a certain accuracy in terms of network approximation, we propose an efficient network training algorithm based on nonlinear optimization. The FNN is first analyzed to obtain those convex regions containing all local solutions. Then, a search is performed constraining to these convex regions for local minima. Since the search is performed over these convex regions, the proposed algorithm can reduce chances of premature algorithm termination due to low gradient values. Preliminary numerical results are also provided to illustrate potential applications.

Original languageEnglish
Pages (from-to)V-383 - V-388
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 1999
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: 1999 Oct 121999 Oct 15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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